Cognitive Analysis and Dictionary Management

ABSTRACT

Embodiments are directed to a system, computer program product, and method for application of cognitive processing to a communication, and selectively transmitting the communication based on the cognitive processing. Natural language understanding (NLU) decomposes the communication to identify content and keywords. A dictionary determined to be contextually related to the communication is identified to support and enable a multi-dimensional analysis of the communication content. The communication is subject to dynamic filtering with support of the dictionary and dictionary content, and the dictionary is subject to a selective amendment based on the dynamic processing. A response is generated from the filtering, and the response is subject to selective transmission.

BACKGROUND

The present embodiments relate to an artificial intelligence platformconfigured to process textual material. More specifically, theembodiments relate to application of linguistics and natural languageunderstanding to the artificial intelligence platform directed atcontent filtering.

In the field of artificial intelligent computer systems, naturallanguage systems (such as the IBM Watson® artificial intelligentcomputer system and other natural language question answering systems)process natural language based on knowledge acquired by the system. Toprocess natural language, the system may be trained with data derivedfrom a database or corpus of knowledge, but the resulting outcome can beincorrect or inaccurate for a variety of reasons relating to thepeculiarities of language constructs and human reasoning.

Machine learning, which is a subset of Artificial intelligence (AI),utilizes algorithms to learn from data and create foresights based onthis data. AI refers to the intelligence when machines, based oninformation, are able to make decisions, which maximizes the chance ofsuccess in a given topic. More specifically, AI is able to learn from adata set to solve problems and provide relevant recommendations. AI is asubset of cognitive computing, which refers to systems that learn atscale, reason with purpose, and naturally interact with humans.Cognitive computing is a mixture of computer science and cognitivescience. Cognitive computing utilizes self-teaching algorithms that useminimum data, visual recognition, and natural language processing tosolve problems and optimize human processes.

At the core of AI and associated reasoning lies the concept ofsimilarity. The process of understanding natural language and objectsrequires reasoning from a relational perspective that can bechallenging. Static structures dictate a determined output or action fora given determinate input. The determined output or action is based onan express relationship within the structure. This arrangement may besatisfactory for select circumstances and conditions. However, it isunderstood that static structures have limitations, and need to adapt toa dynamic environment that is inherently subject to change, and theoutput or action needs to adapt to accommodate the dynamic environment.

SUMMARY

The embodiment described herein includes a system, computer programproduct, and a method for cognitive processing of a communicationtransmission.

In one aspect, a computer system is provided with a processing unit incommunication with memory, and an artificial intelligence (AI) platformoperatively coupled to the processing unit, configured to applycognitive processing to a communication transmission. The AI platform iscomprised of tools to support cognitive assessment and processing. Thetools include, but are not limited to, NL and linguistic managers. TheNL manager decomposes content of an intercepted communication, with thedecomposition identifying one or more keywords in the communication. Thelinguistic manager identifies a dictionary that is contextually relatedto the communication, and functions to invoke a multi-dimensionalanalysis of the communication. The analysis includes comparison ofidentified communication keyword(s) against contextually identifiedkeywords from the dictionary. The NL manager selectively appliescognitive processing to the communication and the keyword comparison.More specifically, the NL manager identifies context of thecommunication and dynamically filters the communication for presence ofthe one or more contextually related keywords in the dictionary. Aresponse from the multi-dimensional analysis is generated, thedictionary is selectively amended, and the communication is selectivelytransmitted based on the response.

In another aspect, a computer program product is provided to applycognitive processing to a communication transmission. The computerprogram product includes a computer readable storage medium withembodied program code that is configured to be executed by a processingunit. Program code is provided to decompose content of an interceptedcommunication. The content decomposition identifies one or more keywordsin the communication. Program code identifies a dictionary that iscontextually related to the communication, and functions to invoke amulti-dimensional analysis of the communication. The analysis includesthe program code to compare the identified communication keyword(s)against contextually identified keywords from the dictionary. Programcode is further provided to selectively apply cognitive processing tothe communication and the keyword comparison, including identificationof context of the communication and further functions to dynamicallyfilter the communication for presence of the one or more contextuallyrelated keywords in the dictionary. A response from themulti-dimensional analysis is generated, the dictionary is subject to aselective amendment, and the communication is selectively transmittedbased on the response.

In an even further aspect, a computer implemented method is provided toapply cognitive processing to a communication transmission. The methoddecomposes content of an intercepted communication. The contentdecomposition identifies one or more keywords in the communication. Adictionary that is contextually related to the communication isidentified, and a multi-dimensional analysis of the communication isinvoked. The analysis includes comparison of the identifiedcommunication keyword(s) against contextually identified keywords fromthe dictionary. Cognitive processing is selectively applied to thecommunication and the keyword comparison, with the processing includingidentification of context of the communication and dynamically filteringthe communication for presence of the one or more contextually relatedkeywords in the dictionary. A response from the multi-dimensionalanalysis is generated, the dictionary is subject to selective amendment,and the communication is selectively transmitted based on the response.

These and other features and advantages will become apparent from thefollowing detailed description of the presently preferred embodiment(s),taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The drawings referenced herein form a part of the specification.Features shown in the drawings are meant as illustrative of only someembodiments, and not of all embodiments unless otherwise explicitlyindicated.

FIG. 1 depicts a block diagram illustrating a text mining system andtools to provide context to word vector and document vectorrepresentations, and linguistic processing responsive to therepresentations.

FIG. 2 depicts a block diagram illustrating the reliability assessmenttools, as shown and described in FIG. 1, and their associatedapplication program interfaces.

FIGS. 3A and 3B together depict a flow chart illustrating cognitivecommunication processing with respect to dynamically amending adictionary of keywords against a communication.

FIG. 4 depicts a block diagram illustrating an example dictionaryhierarchy.

FIG. 5 depicts a flow chart illustrating a process for holisticassessment of transmission of the intercepted transmission.

FIG. 6 depicts a block diagram illustrating an example of a computersystem/server of a cloud based support system, to implement the systemand process described above with respect to FIGS. 1-5.

FIG. 7 depicts a block diagram illustrating a cloud computerenvironment.

FIG. 8 depicts a block diagram illustrating a set of functionalabstraction model layers provided by the cloud computing environment.

DETAILED DESCRIPTION

It will be readily understood that the components of the presentembodiments, as generally described and illustrated in the Figuresherein, may be arranged and designed in a wide variety of differentconfigurations. Thus, the following detailed description of theembodiments of the apparatus, system, and method, as presented in theFigures, is not intended to limit the scope of the embodiments, asclaimed, but is merely representative of selected embodiments.

Reference throughout this specification to “a select embodiment,” “oneembodiment,” or “an embodiment” means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the present embodiments. Thus,appearances of the phrases “a select embodiment,” “in one embodiment,”or “in an embodiment” in various places throughout this specificationare not necessarily referring to the same embodiment.

The illustrated embodiments will be best understood by reference to thedrawings, wherein like parts are designated by like numerals throughout.The following description is intended only by way of example, and simplyillustrates certain selected embodiments of devices, systems, andprocesses that are consistent with the embodiments as claimed herein.

Content filtering, which in one embodiment is also referred to asinformation filtering, employs program code to screen or excludeobjectionable content, such as electronic communication, electronicmessage, web site pages, etc. It is understood in the art that prior artcontent filtering applies specified character strings or keywords,hereinafter referred to collectively as keywords, to identify content tobe excluded or otherwise blocked. However, these prior art systemsutilize static structure for the content filtering, and morespecifically for the character string identification. As shown anddescribed herein computational linguistics is applied to identifylinguistically related elements, and is dynamically applied to thecontent filtering. Computational linguistics is the application ofcomputer science to analysis, synthesis and comprehension of written andspoken language. A computational understanding of language providesinsight into substantive aspects of linguistic elements in theunderlying texts, and improves the relationship between computers andbasic language. One aspect of computational linguistics is directed atbuilding linguistic structures, such as word dictionaries.

As shown and described herein, natural language is a human language,such as English, French, German, etc. Natural language processing (NLP),also referred to herein as computational linguistics, is a component ofArtificial Intelligence (AI). NLP is directed at analyzing andunderstanding the languages that humans use naturally in order tointerface with computers on both written and spoken contexts usingnatural human languages instead of computer language. Specifically, NLPautomates the translation between the computer and human languages.

Natural language understanding (NLU) is a sub-topic of NLP in AI thatpertains to machine reading comprehension. NLU involves modeling humanreading comprehension by parsing and translating input according tonatural language principles. In one embodiment, NLU analyzes text byextracting metadata from content and organizing the extracted metadatainto a multi-level classification hierarchy. The extracted contentsubjected to the hierarchical organization may include, but is notlimited to, concepts, entities, keywords, categories, sentiment,emotion, relations, and semantic roles.

NLP and NLU both pertain to the relationship between natural languageand artificial intelligence. As shown and described herein, NLP is usedto identify content and apply one or more filtering actions on theidentified content based on content filtering rules. In one embodiment,a dictionary of keywords is consulted, and keywords that populate thedictionary function as the filtering protocol. For example, in oneembodiment, a selection of keywords is applied against content, such asa document or website, to determine if any of the keywords are presentin the document or website. NLU is used to address context, and morespecifically to understanding the context in which the keyword(s)identified in the document or website is being used. It is understoodthat not every usage of a keyword(s) would be objectionable, e.g. have anegative implication. The NLU addresses keyword and keyword context, andit also addresses context of the document or website. As shown anddescribed in detail below, the context of the document or web siteentails a holistic assessment in which sentiment(s) expressed in thedocument or web site is derived and applied against one or more policiesintended for the consumer of the content. An Application ProgramInterface (API) is understood in the art as a software intermediarybetween two or more applications, and in one embodiment, one or moreAPI(s) are utilized to perform the above-described NLP and NLU.

As described above, the content filtering is applied against a documentor web site. The content filtering utilizes a dictionary or datastructure, hereinafter referred to collectively as a dictionary, ofkeywords. In one embodiment, the dictionary is a multi-dimensionalorganization that arranges populated keywords by one or morecharacteristics, such as context. It is recognized that in the NLP andthe NLU analysis new and pertinent keywords or string data may have beendiscovered, with the discovery including the context in which the newkeyword is identified. As shown and described below, one or more toolsand processes are provided to identify the new keyword(s), tag thekeyword(s) with a dimension within the dictionary having the contextclassification, and dynamically update the dictionary or data structurewith the discovered new keyword(s) or string data. Accordingly, NLU isapplied to the organization and dynamic growth of the dictionary.

Referring to FIG. 1, a schematic diagram of a computer system (100) isdepicted to cognitive analysis and content filtering. As shown, a server(110) is provided in communication with a plurality of computing devices(180), (182), (184), (186), and (188) across a network connection (105).The server (110) is configured with a processing unit (112) incommunication with memory (116) across a bus (114). The server (110) isshown with an AI platform (150) for document, context, and linguisticprocessing over the network (105) from one or more computing devices(180), (182), (184), (186) and (188). More specifically, the computingdevices (180), (182), (184), (186), and (188) communicate with eachother and with other devices or components via one or more wired and/orwireless data communication links, where each communication link maycomprise one or more of wires, routers, switches, transmitters,receivers, or the like. In this networked arrangement, the server (110)and the network connection (105) enable processing of documents andcontent for one or more content users. Other embodiments of the server(110) may be used with components, systems, sub-systems, and/or devicesother than those that are depicted herein.

The AI platform (150) may be configured to receive input from varioussources or transmit data to one or more devices. For example, AIplatform (150) may receive input from the network (105), one or moreknowledge bases of corpus (160) of electronic documents (162), or otherdata, content users, and other possible sources of input. In selectedembodiments, the knowledge base (160), also referred to herein as acorpus, may include structured, semi-structured, and/or unstructuredcontent in a plurality of documents that are contained in one or morelarge knowledge databases or corpi. The various computing devices (180),(182), (184), (186), and (188) in communication with the network (105)may include access points for content creators and content users. Someof the computing devices may include devices for a database storing thecorpus of data as the body of information used by the AI platform (150),and to process the corpus of data with respect to content and context,thereby enhancing natural language based services. The network (105) mayinclude local network connections and remote connections in variousembodiments, such that the AI platform (150) may operate in environmentsof any size, including local and global, e.g. the Internet.Additionally, the AI platform (150) may serve as a front-end system orin one embodiment a back-end system that can make available a variety ofknowledge extracted from or represented in documents, network accessiblesources and/or structured data sources. In this manner, some processespopulate the AI platform (150) with the AI platform (150) also includinginput interfaces to receive requests and respond accordingly.

As shown, documents or files in the form of one or more electronicdocuments or files are subject to evaluation by the AI platform (150).The knowledge base (160) is shown operatively coupled to the server(110). The knowledge base (160) is shown with structured dictionaries,shown herein as dictionary₀ (164), dictionary) (166), and dictionary₂(168). Although only three dictionaries are shown, the quantity shouldnot be considered limiting. Each dictionary includes a hierarchy ofcontent. An example of a dictionary and the hierarchical structure ofthe dictionaries is shown and described in FIG. 4. The documents subjectto interception and processing by the AI platform (150) may include anystructured and unstructured documents, including but not limited to anyfile, text, article, or source of data (e.g. scholarly articles,dictionary, definitions, encyclopedia references, and the like). In oneembodiment, the intercepted transmission is a web page transmission. Thedocuments may be received from a library, such as the knowledge base(160), or any device operatively coupled to the server (110) across thenetwork (105). Content users may access the AI platform (150) via anetwork connection or an internet connection to the network (105), andmay submit natural language input to the AI platform (150) that mayeffectively be processed into context, such as word vectorrepresentation. As further described below, the context processing, andin one embodiment context representation, enables the content andcorresponding context to be mathematically represented.

As shown, the server (110) is in communication with a knowledge base(160) of dictionaries, or in one embodiment data structures. Theknowledge base (160) functions as a corpus, and in one embodiment, maybe comprised of multiple corpi, including but not limited to acollection of dictionaries and may be a network of dictionarycollections. Alternatively, the knowledge base (160) may function as asingle corpus. The knowledge base (160) is shown locally coupled to theserver (110). In one embodiment, the knowledge base (160) may beoperatively coupled to the server (110) across the network (105). In oneembodiment, the knowledge base (160) may be stored on shared datastorage, such as a cloud shared resource.

As shown, the AI platform (150) is local to the server (110). In someillustrative embodiments, server (110) may be the IBM Watson® systemavailable from International Business Machines Corporation of Armonk,New York, which is augmented with the mechanisms of the illustrativeembodiments described hereafter. As shown, the AI platform (150)includes an information handling system in the form of managers, e.g.tools, including a NL manager (152) and a linguistic manager (154).Though shown as being embodied in or integrated with the server (110),the AI platform (150) and the associated managers (152) and (154) may beimplemented in a separate computing system (e.g., 190) that is connectedacross network (105) to the server (110). Wherever embodied, themanagers (152) and (154) function to provide and assess linguisticanalysis of documents with respect to associated context.

The AI platform (150) intercepts or otherwise receives contenttransmission, and processes the intercepted content prior to continuedtransmission of the content to a recipient or receiving device. Asshown, the AI platform (150) is configured with tools to support thecontent and context processing, including a NL manager (152) and alinguistic manager (154). The NL manager (152) functions to process atransmitted document or file, hereinafter referred to as a document,received across the network (105). The NL manager (152) functions toevaluate and interpret the document, which in one embodiment includesdecomposing the document to identify any keywords present in thedocument. In the case of a web page transmission, the decompositionidentifies web page content and keywords present in the web page. Forexample, in one embodiment, the NL manager (152) removes all stop words,e.g. insignificant words, from the document, and limits the evaluationto all remaining text in the document, e.g. significant words. Thelinguistic manager (154), which is operatively coupled to the NL manager(152), functions to process the keywords against a dictionary. Morespecifically, the linguistic manager (154) identifies a dictionary inthe corpus (160), such as dictionary₁ (164), that is contextuallyrelated to the intercepted communication. Accordingly, the NL manager(152) functions to identify keywords in an intercepted communication,and the linguistic manager (154) identifies and selects a dictionarycontextually related to the communication against which the keywords andthe communication will be processed.

The linguistic manager (154) functions as an interface between theselected or identified dictionary and the document subject toassessment. Specifically, the linguistic manager (154) compares thekeywords identified in the communication against the keywords thatpopulate the dictionary, e.g. dictionary words. This comparison isutilized by the NL manager (152) for cognitive processing of thedocument. In other words, the NL manager (152) uses the comparison toidentify the context of the communication through matching or closelymatching dictionary words and their classified context. The NL manager(152) uses the comparison and contextual identification to selectivelyamend the dictionary. It is understood that the dictionary is not staticin that the dictionary dynamically grows and/or changes over time. Forexample, in one embodiment, the NL manager (152) may identify words inthe document that are semantically related to the dictionary keywords,but are not populated in the dictionary. These semantically relatedwords may be dynamically added to the dictionary by the linguisticmanager (154). It is understood that each of these semantically relateddocument keywords have associated metadata. This metadata may beutilized for classification and positioning of the keywords in thedictionary. In one embodiment, the metadata remains attached to thekeyword after entry in the dictionary. For example, in one embodiment,the keyword has a timestamp identifying when the keyword is added to thedictionary, which may be used at a later point in time for pruning thedictionary.

As shown and described in FIG. 4, the dictionary may be organized into ahierarchy with multiple tiers, with two or more domains, e.g. nodes ineach of the tiers. In one embodiment, the domain is also referred to asa content category, such as finance, health care, government, etc. Thisorganization facilitates contextual identification and evaluation ofcontent. When adding a keyword to the dictionary, the linguistic manager(154) selects or otherwise identifies and selects an appropriatelyclassified dictionary via the hierarchical organization. At the sametime, it is understood that the growth of the dictionary ismulti-dimensional. In one embodiment, the NL manager (152) identifiesone or more secondary or additional keywords in the document that aresemantically related to the keyword being processed, and when thekeyword being processed is identified to be added to the dictionary, thelinguistic manager (154) also processes the semantically relatedkeywords for adding to the dictionary.

The dictionary is subject to growth by adding keywords and semanticallyrelated keywords to the hierarchy. At the same time, the dictionary mayalso support a pruning action, which includes removing words from thedictionary. For example, as mentioned above, as words are added to thedictionary, the added word includes metadata. In one embodiment, themetadata includes at least one field identifying the time the word isadded to the dictionary and the source of the word. These metadatafields are examples, and should not be considered limiting. In oneembodiment, the words may include additional and/or different metadatafields. Using the example fields described, the linguistic manager mayidentify dictionary words based on a metadata field characteristic, suchas the length of time since the word was added or the source of theword, and dynamically and selectively remove the words from thedictionary.

The AI platform (150) and the managers (152) and (154) are shown anddescribed above with respect to dictionary identification and dynamicmanagement. The managers (152) and (154) also include functionality tofilter the intercepted communication, e.g. document, as shown anddescribed in FIG. 5. In addition to the dictionary processing againstthe document that dynamically modifies the dictionary, the NL manager(152) dynamically applies the keyword comparison to the document. Morespecifically, the NL manager conducts a mathematical evaluation of adocument based on a block score of the document words. The algorithm isused for assigning a block score is as follows:

${m{s\left( w_{r} \right)}} = {\underset{i = {r - k}}{\overset{r + k}{\sum\log}}\left( {i + a} \right)*{{bs}\left( w_{i} \right)}}$ms(w_(r)) = ms(w_(r))/log (time  elapsed)

where ms(w_(r)) is the block score of word r which is computed by takingthe weighted logarithmic average of block scores of words which lie inwindow (w_(r−k), w_(r+k)), “a” is a configurable variable, and “k” is awindow size which is a model configurable parameter. In one embodiment,k can be manually configured, or a model can be trained to find anoptimal value of k. The “time elapsed” is the time when the blocked wordwas first identified. Using the block score for the words, the documentas a whole may be computed. The following algorithm is used forcomputing a document score:

${{m\; {s\left( \text{document} \right)}} = {\sum\limits_{word}{{{bs}\left( \text{word} \right)}\text{/total~~words}}}},{\forall{word}}$

The document score is then used to process the document with respect topresentation or transmission. The NL manager (152) assesses the documentwith respect to the document score, and subjects the document to afiltering action, which may have one of a few outcomes. Accordingly, aresponse is generated from the filtering action.

As shown a visual display (170) is operatively coupled to the server(110). The filtering action response may yield presentation of responseoutput (172), such as the document, on the visual display (170), or itmay yield transmission of the document to one or more of the devices(180)-(190) across the network (105). The filtering action may also bein the form of blocking the document from presentation on the visualdisplay or from transmission to one or more devices across the network(105). In one embodiment, the NL manager (152) identifies a URLassociated with the document, and the instituted filter action blocks orprevents presentation or transmission of any documents from the URL.Similarly, in one embodiment, the NL manager (152) may identifyselection portions of the document that are subject to a redaction ormasking, in which case the NL manager (152) redacts or masks thoseportions while enabling other portions to remain unmasked ornon-redacted and to be transmitted to the visual display (170) or to adevice operatively coupled to the server (110) across the network (105).It is understood that one or more of the devices (180)-(190) may be areceiving device to receive the communication, a network entry device,or a network device. The dynamic application of the filtering action tothe communication by the NL manager (152) can be selectively applied toone or more of these receiving devices.

As shown, a data structure, DS (156), is operatively coupled to the NLmanager. The DS (156) is populated with communication entities and theiraddresses, e.g. URLs. The NL manager (152) may selectively add or removeURLs from the list. Similarly, in one embodiment, the DS (156) ismulti-dimensional and reflects relationships of URLs and correspondingdocuments. As the filtering action is applied to the document, the NLmanager (152) dynamically reflects the URL associated with the documentto the DS (156).

It is understood in the art that the document being evaluated may be astructured file. For example, the document may be separated intomultiple content levels, such as chapters, sections, paragraphs, etc.The document assessment shown and described above may be applied on thebasis of the document content level or a selection of content levels,with the application of the filter by NL manager (152) applied theprobabilistic assessment of the designated document content levels.

It is understood that dynamic amendment of the dictionaries enables thedictionaries to respond to current conditions and actions. As thecomposition of the dictionary changes, the cognitive processing by thelinguistic manager (154) and corresponding filter activity of the NLmanager (152) is also subject to change. The linguistic manager (154)supports the dynamic characteristic(s) of the dictionary by supportingand enabling selective addition and removal of dictionary words.Accordingly, the linguistic manager (154) is configured to supportadaptive and dynamic modification.

As shown, the AI platform (150) and the corresponding tools, includingthe NL manager (152) and the linguistic manager (154) integrate contentand context assessment with respect to document processing and dynamicdictionary maintenance. The functionality of each tool is shown anddescribed herein. A result of the content and context assessmentgenerates an outcome of selective document filtering and/or dynamicamendment of the dictionary to exemplify keyword relationships. Types ofinformation handling systems that can utilize the system (110) rangefrom small handheld devices, such as handheld computer/mobile telephone(180) to large mainframe systems, such as mainframe computer (182).Examples of handheld computer (180) include personal digital assistants(PDAs), personal entertainment devices, such as MP4 players, portabletelevisions, and compact disc players. Other examples of informationhandling systems include pen, or tablet, computer (184), laptop, ornotebook, computer (186), personal computer system (188), and server(190). As shown, the various information handling systems can benetworked together using computer network (105). Types of computernetwork (105) that can be used to interconnect the various informationhandling systems include Local Area Networks (LANs), Wireless Local AreaNetworks (WLANs), the Internet, the Public Switched Telephone Network(PSTN), other wireless networks, and any other network topology that canbe used to interconnect the information handling systems. Many of theinformation handling systems include nonvolatile data stores, such ashard drives and/or nonvolatile memory. Some of the information handlingsystems may use separate nonvolatile data stores (e.g., server (190)utilizes nonvolatile data store (190 _(A)), and mainframe computer (182)utilizes nonvolatile data store (182 _(A)). The nonvolatile data store(182 _(A)) can be a component that is external to the variousinformation handling systems or can be internal to one of theinformation handling systems.

An Application Program Interface (API) is understood in the art as asoftware intermediary between two or more applications. With respect tothe AI platform (150) shown and described in FIG. 1, one or more APIsmay be utilized to support one or more of the tools (152) and (154) andtheir associated functionality. Referring to FIG. 2, a block diagram(200) is provided illustrating the tools (152) and (154) and theirassociated APIs. As shown, a plurality of tools are embedded within theknowledge engine (205), with the tools including the NL manager (252)associated with API₀ (212), and the linguistic manager (254) associatedwith API₁ (222). Each of the APIs may be implemented in one or morelanguages and interface specifications. API₀ (212) provides functionalsupport commensurate with the NL manager (152); and API₁ (222) providesfunctional support commensurate with the linguistic manager (154). Asshown, each of the APIs (212) and (222) are operatively coupled to anAPI orchestrator (260), otherwise known as an orchestration layer, whichis understood in the art to function as an abstraction layer totransparently thread together the separate APIs. In one embodiment, thefunctionality of the separate APIs may be joined or combined. As such,the configuration of the APIs shown herein should not be consideredlimiting. Accordingly, as shown herein, the functionality of the toolsmay be embodied or supported by their respective APIs.

Referring together to FIGS. 3A and 3B, a flow chart (300) is provided toillustrate cognitive communication process with respect to dynamicallyamending the dictionary of keywords against the communication isprocessed. As shown, a transmission is intercepted and a correspondingdocument is identified (302). For example, in one embodiment, thetransmission may be directed at a query, the corresponding document maybe identified as responsive to the query. NLP processing is applied tothe document, and more specifically to decompose the document intokeywords (304). The keywords in the document may come in differentforms, including, but not limited to, nouns, verbs, adjectives, andadverbs. A document keyword variable, X_(Total) is assigned to thequantity of keywords identified in the document (306). It is understoodthat the intercepted transmission has corresponding characteristics andcontext. For example, in one embodiment, the characteristics may be thesubject matter of the document, such as science, history, weather, etc.In one embodiment, the document characteristics are identified indocument metadata. Similarly, in one embodiment, NLU is leveraged toidentify document characteristics. In evaluating the interceptedtransmission with respect to a keyword dictionary, it is important toconduct the assessment with respect to an appropriately classifieddictionary. Following step (306) and before conducting a keywordassessment, a dictionary that matches the characteristics and context ofthe transmission is identified or selected (308). Accordingly, thedictionary utilized in the assessment is selected based on thecharacteristics and context of the document.

The dictionary may be a multi-dimensional structure, such as a hierarchyin which items are layered or grouped to reduce complexity. Referring toFIG. 4, a block diagram (400) is provided illustrating an exampledictionary hierarchy. The dictionary is shown with three tiers in thehierarchy, shown herein as Tier₀ (410), Tier₁ (420), and Tier₂ (430).Tier₀ (410) represents a general category of the dictionary in the formof a parent Node_(0,0) (412), which in this example is Science. Tier₁(420) is shown with three example sub-categories of the general categoryand each related to the parent Node_(0,0) (412). The sub-categories areshown in Tier₁ (420) as Node_(1,0) (422), Node _(1,1) (424), andNode_(1,2) (426). In one example, Node_(1,0) (422) may contain keywordsrelated to the topic of Biology, Node_(1,1) (424) may contain keywordsrelated to the topic of Chemistry, and Node_(1,2) (426) may containkeywords related to the topic of Physics. As further shown, Tier₂ (430)includes further sub-categories for each node represents in Tier₁ (420).As shown, Tier₂ (430) includes Node_(2,0) (432), Node_(2,1) (434),Node_(2,2) (436), Node_(2,3) (438), Node_(2,4) (440), Node_(2,5) (442),Node_(2,6) (444), Node_(2,7) (446), and Node_(2,8) (448) that representkeywords in each sub-category as related to a topic within thesub-category. For example, in one embodiment, Node_(2,0) (432) isdirected to topic₀, Node_(2,1) (434) is directed to topic₁, Node_(2,2)(436) is directed to topic₂, Node_(2,3) (438) is directed to topic₃,Node_(2,4) (440) is directed to topic₄, Node_(2,5) (442) is directed totopic₅, Node_(2,6) (444) is directed to topic₆, Node_(2,7) (446) isdirected to topic₇, and Node_(2,8) (448) is directed to topic₈.Accordingly, the hierarchical representation of the dictionarydemonstrates the organization and layering of groups.

Following the dictionary identification or selection at step (308), atier within the dictionary that matches the context of the transmissionis identified (310). Thereafter, the keyword counting variable, X, isinitialized (312), the identified tier of the dictionary is leveraged tosearch for matching keywords (314). It is then determined if anykeywords have been identified in the dictionary for keyword_(X) from thetransmission (316). A negative response to the determination is followedby an increment of the document keyword counting variable, X, (318),followed by an assessment to determine if all of the document keywordshave been search in the dictionary (320). A negative response at to thedetermination is followed by a return to step (314). Accordingly, thedictionary is utilized as a source to identify keywords that match theidentified transmission keywords.

A positive response to the determination at step (316) is an indicationthat there is at least one matching keyword found or otherwiseidentified in the dictionary. Cognitive processing is applied to thetransmission content to identify context and to filter the communicationfor contextually related words to keyword_(X) (322). The variableY_(Total) is assigned to the quantity of contextually identified wordsin the communication (324). NLU is applied to the metadata for each ofthe related words, word_(Y), to identify metadata, such as sentiment,e.g. positive, negative, or neutral, match word_(Y) to a specific tieror layer in the dictionary, and selectively amend the dictionary basedon the identified metadata (326). The dictionary amendment may includeadding one or more of the contextually related words to the dictionary,or in one embodiment, removing a corresponding keyword from thedictionary. The selective amendment is conducted dynamically andprovides dynamic characteristics to the dictionary, e.g. the dictionaryresponds to the transmission assessment. As shown at step (326), thedictionary may be subject to pruning by selectively removing words. Forexample, in one embodiment, the words may be removed based on timeelapse to reflect the latest context filtering. Accordingly, theamendment of the dictionary includes selectively adding and/or removingwords.

Following the dictionary amendment processing at step (326), the processreturns to step (318) for continued keyword processing. Once all of theidentified keywords have been assessed, as shown by a negative responseat step (320), the dictionary amendment process concludes. Accordingly,as shown and described in FIGS. 3A and 3B, the dictionary is subject todynamic maintenance and amendment.

In addition to dictionary maintenance, the intercepted transmission issubject to a holistic assessment transmission to a recipient. Referringto FIG. 5, a flow chart (500) is provided to illustrate a process forholistic assessment of the intercepted transmission. As shown, thetransmission is intercepted (502) and subject to NLU to decompose thecommunication with respect to context (504). A dictionary determined tobe contextually related to the intercepted communication is identified(506). In addition, keywords in the intercepted communication areidentified (508), with the quantity assigned to the variable X_(Total)(510). Using the dictionary, the document is assessed with respect tothe identified keywords, including assessing a matching score to each ofthe identified keywords_(X) (512). Using the assigned matching scorefrom step (512), a document score is computed (514). The document scoreis utilized as a value against which document transmission isassessment. As shown herein, the document score is assessed against athreshold value (516). In one embodiment, the threshold value isconfigurable. If the document score is greater than or equal to thethreshold, then the document transmission continues and the document ispresented to an intended or identified recipient (518). Similarly, ifthe document score is less than the threshold, then the document issubject to filtering (520). Accordingly, the document score utilizes theblock score to dynamically assess the content and context of thedocument for selective filtering and presentation.

The document filtering at step (520) can take on different forms. Forexample, the document filtering may be directed at identifying a uniformresource locator (URL) for the document, and blocking all documentsassociated with the identified URL, including the document subject toassessment. Similarly, in one embodiment, the document filtering blocksa domain, e.g. content category, corresponding to the communication.These are examples of blocking or otherwise preventing documenttransmission. In one embodiment, the document filtering enables documenttransmission while masking select text or textual components in thedocument that is transmitted. The masking utilizes NLP and aprobabilistic assessment to identify select words or phrases in thedocument and to mask the identified words or phrases.

The document filtering shown and described in FIG. 5 is described on thelevel of a document and document transmission. It is understood that thefiltering can be expanded or applied to another level of selectivetransmission. For example, in one embodiment, the filtering maydetermine that the document, or in one embodiment the URL associatedwith the intercepted document, should be prevented from transmission toa network entry device, all network devices, or a selection of networkdevices. Accordingly, the selective transmission may be expanded toprevent transmission of a document into a network of computing devices.

Embodiments may also be in the form of a computer program device for usewith an intelligent computer platform in order to assist the AI platform(150) to identify one or more mathematically related candidates. Thedevice has program code embodied therewith. The program code isexecutable by a processing unit to execute the functionality of thetools of the AI platform (150), e.g. the NL manager (152) and thelinguistic manager (154). Aspects of the functional tools, e.g. NLmanager and linguistic manager, and their associated functionality maybe embodied in a computer system/server in a single location, or in oneembodiment, may be configured in a cloud based system sharing computingresources.

With references to FIG. 6, a block diagram (600) is providedillustrating an example of a computer system/server (602), hereinafterreferred to as a host (602) in communication with a cloud based supportsystem, to implement the processes described above with respect to FIGS.1-5. Host (602) is operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well-known computing systems, environments, and/orconfigurations that may be suitable for use with host (602) include, butare not limited to, personal computer systems, server computer systems,thin clients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and file systems (e.g., distributed storageenvironments and distributed cloud computing environments) that includeany of the above systems, devices, and their equivalents.

Host (602) may be described in the general context of computersystem-executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.Host (602) may be practiced in distributed cloud computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network. In a distributed cloud computingenvironment, program modules may be located in both local and remotecomputer system storage media including memory storage devices.

As shown in FIG. 6, host (602) is shown in the form of a general-purposecomputing device. The components of host (602) may include, but are notlimited to, one or more processors or processing units (604), e.g.hardware processors, a system memory (606), and a bus (608) that couplesvarious system components including system memory (606) to processor(604). Bus (608) represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus. Host (602) typicallyincludes a variety of computer system readable media. Such media may beany available media that is accessible by host (602) and it includesboth volatile and non-volatile media, removable and non-removable media.

Memory (606) can include computer system readable media in the form ofvolatile memory, such as random access memory (RAM) (630) and/or cachememory (632). By way of example only, storage system (634) can beprovided for reading from and writing to a non-removable, non-volatilemagnetic media (not shown and typically called a “hard drive”). Althoughnot shown, a magnetic disk drive for reading from and writing to aremovable, non-volatile magnetic disk (e.g., a “floppy disk”), and anoptical disk drive for reading from or writing to a removable,non-volatile optical disk such as a CD-ROM, DVD-ROM or other opticalmedia can be provided. In such instances, each can be connected to bus(608) by one or more data media interfaces.

Program/utility (640), having a set (at least one) of program modules(642), may be stored in memory (606) by way of example, and notlimitation, as well as an operating system, one or more applicationprograms, other program modules, and program data. Each of the operatingsystems, one or more application programs, other program modules, andprogram data or some combination thereof, may include an implementationof a networking environment. Program modules (642) generally carry outthe functions and/or methodologies of embodiments supported by the AIplatform (150) and the tools (152) and (154). For example, the set ofprogram modules (642) may include the modules configured as the NL andlinguistic managers, as described in FIGS. 1-5.

Host (602) may also communicate with one or more external devices (614),such as a keyboard, a pointing device, etc.; a display (624); one ormore devices that enable a user to interact with host (602); and/or anydevices (e.g., network card, modem, etc.) that enable host (602) tocommunicate with one or more other computing devices. Such communicationcan occur via Input/Output (I/O) interface(s) (622). Still yet, host(602) can communicate with one or more networks such as a local areanetwork (LAN), a general wide area network (WAN), and/or a publicnetwork (e.g., the Internet) via network adapter (620). As depicted,network adapter (620) communicates with the other components of host(602) via bus (608). In one embodiment, a plurality of nodes of adistributed file system (not shown) is in communication with the host(602) via the I/O interface (622) or via the network adapter (620). Itshould be understood that although not shown, other hardware and/orsoftware components could be used in conjunction with host (602).Examples, include, but are not limited to: microcode, device drivers,redundant processing units, external disk drive arrays, RAID systems,tape drives, and data archival storage systems, etc.

In this document, the terms “computer program medium,” “computer usablemedium,” and “computer readable medium” are used to generally refer tomedia such as main memory (606), including RAM (630), cache (632), andstorage system (634), such as a removable storage drive and a hard diskinstalled in a hard disk drive.

Computer programs (also called computer control logic) are stored inmemory (606). Computer programs may also be received via a communicationinterface, such as network adapter (620). Such computer programs, whenrun, enable the computer system to perform the features of the presentembodiments as discussed herein. In particular, the computer programs,when run, enable the processing unit (604) to perform the features ofthe computer system. Accordingly, such computer programs representcontrollers of the computer system.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a dynamic or static random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), a magnetic storage device, a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present embodiments may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server or cluster of servers. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the embodiments.

In one embodiment, host (602) is a node of a cloud computingenvironment. As is known in the art, cloud computing is a model ofservice delivery for enabling convenient, on-demand network access to ashared pool of configurable computing resources (e.g., networks, networkbandwidth, servers, processing, memory, storage, applications, virtualmachines, and services) that can be rapidly provisioned and releasedwith minimal management effort or interaction with a provider of theservice. This cloud model may include at least five characteristics, atleast three service models, and at least four deployment models. Exampleof such characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher layerof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some layer ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting for loadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 7, an illustrative cloud computing network (700).As shown, cloud computing network (700) includes a cloud computingenvironment (750) having one or more cloud computing nodes (710) withwhich local computing devices used by cloud consumers may communicate.Examples of these local computing devices include, but are not limitedto, personal digital assistant (PDA) or cellular telephone (754A),desktop computer (754B), laptop computer (754C), and/or automobilecomputer system (754N). Individual nodes within nodes (710) may furthercommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment (700) to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices (754A-N)shown in FIG. 7 are intended to be illustrative only and that the cloudcomputing environment (750) can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 8, a set of functional abstraction layers (800)provided by the cloud computing network of FIG. 7 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 8 are intended to be illustrative only, and the embodiments arenot limited thereto. As depicted, the following layers and correspondingfunctions are provided: hardware and software layer (810),virtualization layer (820), management layer (830), and workload layer(840). The hardware and software layer (810) includes hardware andsoftware components. Examples of hardware components include mainframes,in one example IBM® zSeries® systems; RISC (Reduced Instruction SetComputer) architecture based servers, in one example IBM pSeries®systems; IBM xSeries® systems; IBM BladeCenter® systems; storagedevices; networks and networking components. Examples of softwarecomponents include network application server software, in one exampleIBM WebSphere® application server software; and database software, inone example IBM DB2® database software. (IBM, zSeries, pSeries, xSeries,BladeCenter, WebSphere, and DB2 are trademarks of International BusinessMachines Corporation registered in many jurisdictions worldwide).

Virtualization layer (820) provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer (830) may provide the followingfunctions: resource provisioning, metering and pricing, user portal,service layer management, and SLA planning and fulfillment. Resourceprovisioning provides dynamic procurement of computing resources andother resources that are utilized to perform tasks within the cloudcomputing environment. Metering and pricing provides cost tracking asresources are utilized within the cloud computing environment, andbilling or invoicing for consumption of these resources. In one example,these resources may comprise application software licenses. Securityprovides identity verification for cloud consumers and tasks, as well asprotection for data and other resources. User portal provides access tothe cloud computing environment for consumers and system administrators.Service layer management provides cloud computing resource allocationand management such that required service layers are met. Service LayerAgreement (SLA) planning and fulfillment provides pre-arrangement for,and procurement of, cloud computing resources for which a futurerequirement is anticipated in accordance with an SLA.

Workloads layer (840) provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include, but are notlimited to: mapping and navigation; software development and lifecyclemanagement; virtual classroom education delivery; data analyticsprocessing; transaction processing; and cognitive assessment andmanagement.

As will be appreciated by one skilled in the art, the aspects may beembodied as a system, method, or computer program product. Accordingly,the aspects may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.), or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “circuit,”“module,” or “system.” Furthermore, the aspects described herein maytake the form of a computer program product embodied in one or morecomputer readable medium(s) having computer readable program codeembodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

The embodiments are described above with reference to flow chartillustrations and/or block diagrams of methods, apparatus (systems), andcomputer program products. It will be understood that each block of theflow chart illustrations and/or block diagrams, and combinations ofblocks in the flow chart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flow chart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flow chart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions, which execute on thecomputer or other programmable apparatus, provide processes forimplementing the functions/acts specified in the flow chart and/or blockdiagram block or blocks.

The flow charts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments. In this regard, each block in the flow charts or blockdiagrams may represent a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flow chart illustration(s), and combinations ofblocks in the block diagrams and/or flow chart illustration(s), can beimplemented by special purpose hardware-based systems that perform thespecified functions or acts, or combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a”, “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises” and/or “comprising,”when used in this specification, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

The embodiments described herein may be implemented in a system, amethod, and/or a computer program product. The computer program productmay include a computer readable storage medium (or media) havingcomputer readable program instructions thereon for causing a processorto carry out the embodiments described herein.

The embodiments are described herein with reference to flow chartillustrations and/or block diagrams of methods, apparatus (systems), andcomputer program products. It will be understood that each block of theflow chart illustrations and/or block diagrams, and combinations ofblocks in the flow chart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flow chart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flow chart and/or block diagram blockor blocks.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present embodiments has been presented for purposesof illustration and description, but is not intended to be exhaustive orlimited to the embodiments in the form disclosed.

Indeed, executable code could be a single instruction, or manyinstructions, and may even be distributed over several different codesegments, among different applications, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within the tool, and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single dataset, or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, as electronic signals on a system or network.

Furthermore, the described features, structures, or characteristics maybe combined in any suitable manner in one or more embodiments. In thefollowing description, numerous specific details are provided, such asexamples of agents, to provide a thorough understanding of the disclosedembodiments. One skilled in the relevant art will recognize, however,that the embodiments can be practiced without one or more of thespecific details, or with other methods, components, materials, etc. Inother instances, well-known structures, materials, or operations are notshown or described in detail to avoid obscuring aspects of theembodiments.

Many modifications and variations will be apparent to those of ordinaryskill in the art without departing from the scope and spirit of theembodiments. The embodiment was chosen and described in order to bestexplain the principles of the embodiments and the practical application,and to enable others of ordinary skill in the art to understand theembodiments for various embodiments with various modifications as aresuited to the particular use contemplated. Accordingly, the linguisticapplication provides context sensitivity with respect to documentassessment and dictionary management.

While particular embodiments of the present embodiments have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, changes and modifications may be madewithout departing from the embodiments and its broader aspects.Therefore, the appended claims are to encompass within their scope allsuch changes and modifications as within the true spirit and scope ofthe embodiments. Furthermore, it is to be understood that theembodiments are solely defined by the appended claims. It will beunderstood by those with skill in the art that if a specific number ofan introduced claim element is intended, such intent will be explicitlyrecited in the claim, and in the absence of such recitation no suchlimitation is present. For non-limiting examples, as an aid tounderstanding, the following appended claims contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimelements. However, the use of such phrases should not be construed toimply that the introduction of a claim element by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim element to embodiments containing only one suchelement, even when the same claim includes the introductory phrases “oneor more” or “at least one” and indefinite articles such as “a” or “an”;the same holds true for the use in the claims of definite articles.

It will be appreciated that, although specific embodiments have beendescribed herein for purposes of illustration, various modifications maybe made without departing from the spirit and scope of the embodiments.Accordingly, the scope of protection of the embodiments is limited onlyby the following claims and their equivalents.

What is claimed is:
 1. A computer system comprising: a processing unitoperatively coupled to memory; an artificial intelligence platform incommunication with the processing unit to apply cognitive processing tocommunication transmission, the platform including: a natural language(NL) manager to decompose content of an intercepted communication, andidentify one or more keywords in the communication; a linguistic manageroperatively coupled to the natural language (NL) manager, the linguisticmanager to identify a dictionary contextually related to the interceptedcommunication, and invoke a multi-dimensional analysis of theintercepted message, including: compare the one or more identifiedcommunication keywords against dictionary keywords for the contextuallyidentified dictionary; the NL manager to selectively apply cognitiveprocessing to the intercepted communication and the keyword comparison,including identify context of the communication, and dynamically filterthe communication for one or more contextually related keywords; and aresponse generated from the multi-dimensional analysis, includingselective amendment of the dictionary, and transmission of the generatedresponse.
 2. The system of claim 1, further comprising the NL manager toidentify one or more additional keywords in the interceptedcommunication responsive to the cognitive processing, the one or moreadditional keywords semantically related to the context of theidentified dictionary.
 3. The system of claim 2, further comprising thelinguistic manager to dynamically add the one or more additionalkeywords to the dictionary, and each of the added keywords includeskeyword metadata.
 4. The system of claim 3, wherein the dictionaryincludes two or more categories of keywords, and the dynamic addition ofthe one or more additional keywords to the dictionary further comprisingthe linguistic manager to identify at least one of the two or morecategories of keywords, the categories grouped an element selected fromthe group consisting of: domain and sentiment, and select the identifiedcategory for receipt of the one or more additional keywords.
 5. Thesystem of claim 2, further comprising the NL manager to identify one ormore secondary keywords semantically related to the one or moreadditional keywords, and the linguistic manager to dynamically add theone or more identified keywords to the identified dictionary.
 6. Thesystem of claim 2, further comprising the linguistic manager todynamically remove one or more keywords from the dictionary, wherein thedynamic removal is selective based on the keyword metadata.
 7. Thesystem of claim 6, further comprising the NL manager to assess aprobabilistic block score to the identified communication keywords, theprobabilistic block score including a time elapsed factor, and toincorporate the time elapsed factor into the keyword metadata.
 8. Acomputer program product for applying cognitive processing tocommunication transmission, the computer program product comprising acomputer readable storage device having program code embodied therewith,the program code executable by a processor to: apply natural languageunderstanding (NL) to decompose content of an intercepted communication,and identify one or more keywords in the communication; identify adictionary contextually related to the intercepted communication, andinvoke a multi-dimensional analysis of the intercepted message,including: compare the one or more identified communication keywordsagainst dictionary keywords for the contextually identified dictionary;and selectively apply cognitive processing to the interceptedcommunication and the keyword comparison, including identify context ofthe communication, and dynamically filter the communication for one ormore contextually related keywords; and a response generated from themulti-dimensional analysis, including selective amendment of thedictionary, and transmission of the generated response.
 9. The computerprogram product of claim 8, further comprising program code to identifyone or more additional keywords in the intercepted communicationresponsive to the cognitive processing, the one or more additionalkeywords semantically related to the context of the identifieddictionary.
 10. The computer program product of claim 9, furthercomprising program code to dynamically add the one or more additionalkeywords to the dictionary, and each of the added keywords includeskeyword metadata.
 11. The computer program product of claim 10, whereinthe dictionary includes two or more categories of keywords, and thedynamic addition of the one or more additional keywords to thedictionary further comprising program code to identify at least one ofthe two or more categories of keywords, the categories grouped by anelement selected from the group consisting of: domain and sentiment, andselect the identified category for receipt of the one or more additionalkeywords.
 12. The computer program product of claim 9, furthercomprising program code to identify one or more secondary keywordssemantically related to the one or more additional keywords, and todynamically add the one or more identified keywords to the identifieddictionary.
 13. The computer program product of claim 9, furthercomprising program code to dynamically remove one or more keywords fromthe dictionary, wherein the dynamic removal is selective based on thekeyword metadata.
 14. The computer program product of claim 13, furthercomprising program code to assess a probabilistic block score to theidentified communication keywords, the probabilistic block scoreincluding a time elapsed factor, and to incorporate the time elapsedfactor into the keyword metadata.
 15. A method comprising: interceptinga communication, and utilizing natural language understanding (NLU) todecompose communication content, including identifying one or morekeywords in the communication; identifying a dictionary contextuallyrelated to the intercepted communication, and invoking amulti-dimensional analysis of the intercepted communication, theanalysis including: comparing the one or more identified communicationkeywords against dictionary keywords for the contextually identifieddictionary; and selectively applying cognitive processing to theintercepted communication and the keyword comparison, includingidentifying context of the communication, and dynamically filtering thecommunication for one or more contextually related keywords; a responsegenerated from the multi-dimensional analysis, including selectivelyamending the dictionary, and transmitting the generated response. 16.The method of claim 15 further comprising identifying one or moreadditional keywords in the intercepted communication responsive to thecognitive processing, the one or more additional keywords semanticallyrelated to the context of the identified dictionary.
 17. The method ofclaim 16, further comprising dynamically adding the one or moreadditional keywords to the dictionary, and each of the added keywordsincludes keyword metadata.
 18. The method of claim 17, wherein thedictionary includes two or more categories of keywords, and the dynamicaddition of the one or more additional keywords to the dictionaryfurther comprising identifying at least one of the two or morecategories of keywords based an element selected from the groupconsisting of: domain and sentiment, and selecting the identifiedcategory for receipt of the one or more additional keywords.
 19. Themethod of claim 16, further comprising identifying one or more secondarykeywords semantically related to the one or more additional keywords,and dynamically amending dictionary content, the amending including anaction selected from the group consisting of: adding the one or moreidentified keywords to the identified dictionary and removing one ormore keywords from the dictionary, wherein the dynamic removal isselective based on the keyword metadata.
 20. The method of claim 19,further comprising assessing a probabilistic block score to theidentified communication keywords, the probabilistic block scoreincluding a time elapsed factor, and incorporated the time elapsedfactor into the keyword metadata.